ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Evaluation of provider documentation patterns as a tool to deliver ongoing patient-centred diabetes education and support.International journal of clinical practice · 2020Article
- Hybrid bag of approaches to characterize selection criteria for cohort identification.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.Journal of biomedical informatics · 2019Article
- Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.Population health management · 2019Review
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Cost-sensitive Active Learning for Phenotyping of Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Article
- Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.Journal of biomedical informatics · 2015Article
- Extracting research-quality phenotypes from electronic health records to support precision medicine.Genome medicine · 2015Article
- Limestone: high-throughput candidate phenotype generation via tensor factorization.Journal of biomedical informatics · 2014Article
- Identifying and mitigating biases in EHR laboratory tests.Journal of biomedical informatics · 2014Article
- Applying active learning to high-throughput phenotyping algorithms for electronic health records data.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.International journal of medical informatics · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
unlabelledCurrent research on high throughput identification of patients with a specific phenotype is in its infancy. There is an urgent need to develop a general automatic approach for patient identification.
objectiveWe took advantage of Mayo Clinic electronic clinical notes and proposed a novel method of combining NLP, machine learning, and ontology for automatic patient identification. We also investigated the benefits of involving existing SNOMED semantic knowledge in a patient identification task.
methodsthe SVM algorithm was applied on SNOMED concept units extracted from T2DM case/control clinical notes. Precision, recall, and F-score were calculated to evaluate the performance.
resultsThis approach achieved an F-score of above 0.950 for both groups when using all identified concept units as features. Concept units from semantic type-Disease or Syndrome contain the most important information for patient identification. Our results also implied that the coarse level concepts contain enough information to classify T2DM cases/controls.
Indexed as
Identifiers
21347100PMC3041302What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.